Aggregation of reputation data

ABSTRACT

Methods and systems for operation upon one or more data processors for aggregating reputation data from dispersed reputation engines and deriving global reputation information for use in handling received communications.

CROSS-REFERENCE

This application claims priority to U.S. patent application Ser. No.11/142,943, entitled “Systems and Methods for Classification ofMessaging Entities,” filed on Jun. 2, 2005, and to U.S. patentapplication Ser. No. 11/173,941, entitled “Message Profiling Systems andMethods,” filed on Jul. 1, 2005.

This application incorporates by reference, in their entirety and forall purposes, commonly assigned U.S. patent applications:

Application No. Title Filing Date 10/094,211 “Systems and Methods forEnhancing Mar. 8, 2002 Electronic Communication Security” 10/361,067“Systems and Methods for Automated Feb. 7, 2003 Whitelisting inMonitored Communications” 10/373,325 “Systems and Methods for UpstreamFeb. 24, 2003 Threat Pushback” 10/384,924 “Systems and Methods forSecure Mar. 6, 2003 Communication Delivery” 11/142,943 “Systems andMethods for Jun. 2, 2005 Classification of Messaging Entities”11/173,941 “Message Profiling Systems and Jul. 1, 2005 Methods”11/388,575 “Systems and Methods for Message Mar. 24, 2006 ThreatManagement” 11/456,803 “Systems And Methods For Adaptive Jul. 11, 2006Message Interrogation Through Multiple Queues” 11/456,765 “Systems andMethods For Anomaly Jul. 11, 2006 Detection in Patterns of MonitoredCommunications” 11/423,313 “Systems and Methods for Jun. 9, 2006Identifying Potentially Malicious Messages” 11/456,954 “Systems andMethods For Message Jul. 12, 2006 Threat Management” 11/456,960 “Systemsand Methods For Message Jul. 12, 2006 Threat Management” 11/423,308“Systems and Methods for Jun. 9, 2006 Graphically Displaying MessagingTraffic” 11/383,347 “Content-Based Policy Compliance May 15, 2006Systems and Methods” 11/423,329 “Methods and Systems for Exposing Jun.9, 2006 Messaging Reputation to an End User”

This application incorporates by reference, in their entirety and forall purposes, commonly assigned U.S. Patents:

Patent No. Title Filing Date 6,941,467 “Systems and Methods for AdaptiveMar. 8, 2002 Message Interrogation through Multiple Queues” 7,089,590“Systems and Methods for Adaptive Sep. 2, 2005 Message Interrogationthrough Multiple Queues” 7,096,498 “Systems and Methods for Message Feb.7, 2003 Threat Management” 7,124,438 “Systems and Methods for AnomalyMar. 8, 2002 Detection in Patterns of Monitored Communications”

Technical Field

This document relates generally to systems and methods for processingcommunications and more particularly to systems and methods forclassifying entities associated with communications.

BACKGROUND

In the anti-spam industry, spammers use various creative means forevading detection by spam filters. As such, the entity from which acommunication originated can provide another indication of whether agiven communication should be allowed into an enterprise networkenvironment.

However, current tools for message sender analysis include internetprotocol (IP) blacklists (sometimes called real-time blacklists (RBLs))and IP whitelists (real-time whitelists (RWLs)). Whitelists andblacklists certainly add value to the spam classification process;however, whitelists and blacklists are inherently limited to providing abinary-type (YES/NO) response to each query. Moreover, blacklists andwhitelists treat entities independently, and overlook the evidenceprovided by various attributes associated with the entities.

SUMMARY

Systems and methods used to aggregate reputation information areprovided. Systems used to aggregate reputation information can include acentralized reputation engine and an aggregation engine. The centralizedreputation engine can receive feedback from a plurality of localreputation engines. The aggregation engine can derive a globalreputation for a queried entity based upon an aggregation of theplurality of local reputations. The centralized reputation engine canfurther provide the global reputation of the queried entity to a localreputation engines responsive to receiving a reputation query from thelocal reputation engine.

Methods of aggregating reputation information can include: receiving areputation query from a requesting local reputation engine; retrieving aplurality of local reputations the local reputations being respectivelyassociated with a plurality of local reputation engines; aggregating theplurality of local reputations; deriving a global reputation from theaggregation of the local reputations; and, responding to the reputationquery with the global reputation.

Examples of computer readable media operating on a processor aggregatelocal reputation data to produce a global reputation vector, can performthe steps of: receiving a reputation query from a requesting localreputation engine; retrieving a plurality of local reputations the localreputations being respectively associated with a plurality of localreputation engines; aggregating the plurality of local reputations;deriving a global reputation from the aggregation of the localreputations; and, responding to the reputation query with the globalreputation.

Other example reputation aggregations systems can include acommunications interface and a reputation engine. The communicationsinterface can receive global reputation information from a centralserver, the global reputation being associated with an entity. Thereputation engine can bias the global reputation received from thecentral server based upon defined local preferences.

Further example reputation aggregation systems can include acommunications interface, a reputation module and a traffic controlmodule. The communications interface can receive distributed reputationinformation from distributed reputation engines. The reputation modulecan aggregate the distributed reputation information and derive a globalreputation based upon the aggregation of the distributed reputationinformation, the reputation module can also derive a local reputationinformation based upon communications received by the reputation module.The traffic control module can determine handling associated withcommunications based upon the global reputation and the localreputation.

DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram depicting an example network in which systemsand methods of this disclosure can operate.

FIG. 2 is a block diagram depicting an example network architecture ofthis disclosure.

FIG. 3 is a block diagram depicting an example of communications andentities including identifiers and attributes used to detectrelationships between entities.

FIG. 4 is a flowchart depicting an operational scenario used to detectrelationships and assign risk to entities.

FIG. 5 is a block diagram illustrating an example network architectureincluding local reputations stored by local security agents and a globalreputation stored by one or more servers.

FIG. 6 is a block diagram illustrating a determination of a globalreputation based on local reputation feedback.

FIG. 7 is a flow diagram illustrating an example resolution between aglobal reputation and a local reputation.

FIG. 8 is an example graphical user interface for adjusting the settingsof a filter associated with a reputation server.

FIG. 9 is a block diagram illustrating reputation based connectionthrottling for voice over internet protocol (VoIP) or short messageservice (SMS) communications.

FIG. 10 is a block diagram illustrating a reputation based loadbalancer.

FIG. 11A is a flowchart illustrating an example operational scenario forgeolocation based authentication.

FIG. 11B is a flowchart illustrating another example operationalscenario for geolocation based authentication.

FIG. 11C is a flowchart illustrating another example operationalscenario for geolocation based authentication.

FIG. 12 is a flowchart illustrating an example operational scenario fora reputation based dynamic quarantine.

FIG. 13 is an example graphical user interface display of an image spamcommunication.

FIG. 14 is a flowchart illustrating an example operational scenario fordetecting image spam.

FIG. 15A is a flowchart illustrating an operational scenario foranalyzing the structure of a communication.

FIG. 15B is a flowchart illustrating an operational scenario foranalyzing the features of an image.

FIG. 15C is a flowchart illustrating an operational scenario fornormalizing the an image for spam processing.

FIG. 15D is a flowchart illustrating an operational scenario foranalyzing the fingerprint of an image to find common fragments amongmultiple images.

DETAILED DESCRIPTION

FIG. 1 is a block diagram depicting an example network environment inwhich systems and methods of this disclosure can operate. Security agent100 can typically reside between a firewall system (not shown) andservers (not shown) internal to a network 110 (e.g., an enterprisenetwork). As should be understood, the network 110 can include a numberof servers, including, for example, electronic mail servers, webservers, and various application servers as may be used by theenterprise associated with the network 110.

The security agent 100 monitors communications entering and exiting thenetwork 110. These communications are typically received through theinternet 120 from many entities 130 a-f that are connected to theinternet 120. One or more of the entities 130 a-f can be legitimateoriginators of communications traffic. However, one or more of theentities 130 a-f can also be non-reptutable entities originatingunwanted communications. As such, the security agent 100 includes areputation engine. The reputation engine can inspect a communication andto determine a reputation associated with an entity that originated thecommunication. The security agent 100 then performs an action on thecommunication based upon the reputation of the originating entity If thereputation indicates that the originator of the communication isreputable, for example, the security agent can forward the communicationto the recipient of the communication. However, if the reputationindicates that the originator of the communication is non-reputable, forexample, the security agent can quarantine the communication, performmore tests on the message, or require authentication from the messageoriginator, among many others. Reputation engines are described indetail in U.S. Patent Publication No. 2006/0015942, which is herebyincorporated by reference.

FIG. 2 is a block diagram depicting an example network architecture ofthis disclosure. Security agents 100 a-n are shown logically residingbetween networks 110 a-n, respectively, and the internet 120. While notshown in FIG. 2, it should be understood that a firewall may beinstalled between the security agents 100 a-n and the internet 120 toprovide protection from unauthorized communications from entering therespective networks 110 a-n. Moreover, intrusion detection systems (IDS)(not shown) can be deployed in conjunction with firewall systems toidentify suspicious patterns of activity and to signal alerts when suchactivity is identified.

While such systems provide some protection for a network they typicallydo not address application level security threats. For example, hackersoften attempt to use various network-type applications (e.g., e-mail,web, instant messaging (IM), etc.) to create a pre-textual connectionwith the networks 110 a-n in order to exploit security holes created bythese various applications using entities 130 a-e. However, not allentities 130 a-e imply threats to the network 110 a-n. Some entities 130a-e originate legitimate traffic, allowing the employees of a company tocommunicate with business associates more efficiently. While examiningthe communications for potential threats is useful, it can be difficultto maintain current threat information because attacks are beingcontinually modified to account for the latest filtering techniques.Thus, security agents 100 a-n can run multiple tests on a communicationto determine whether the communication is legitimate.

Furthermore, sender information included in the communication can beused to help determine whether or not a communication is legitimate. Assuch, sophisticated security agents 100 a-n can track entities andanalyze the characteristics of the entities to help determine whether toallow a communication to enter a network 110 a-n. The entities 110 a-ncan then be assigned a reputation. Decisions on a communication can takeinto account the reputation of an entity 130 a-e that originated thecommunication. Moreover, one or more central systems 200 can collectinformation on entities 120 a-e and distribute the collected data toother central systems 200 and/or the security agents 100 a-n.

Reputation engines can assist in identifying the bulk of the maliciouscommunications without extensive and potentially costly local analysisof the content of the communication. Reputation engines can also help toidentify legitimate communications and prioritize their delivery andreduce the risk of misclassifying a legitimate communication. Moreover,reputation engines can provide a dynamic and predictive approaches tothe problem of identifying malicious, as well as legitimate,transactions in physical or virtual worlds. Examples include the processof filtering malicious communications in an email, instant messaging,VoIP, SMS or other communication protocol system using analysis of thereputation of sender and content. A security agent 100 a-n can thenapply a global or local policy to determine what action to perform withrespect to the communication (such as deny, quarantine, load balance,deliver with assigned priority, analyze locally with additionalscrutiny) to the reputation result.

However, the entities 130 a-e can connect to the internet in a varietyof methods. As should be understood, an entity 130 a-e can have multipleidentifiers (such as, for example, e-mail addresses, IP addresses,identifier documentation, etc) at the same time or over a period oftime. For example, a mail server with changing IP addresses can havemultiple identities over time. Moreover, one identifier can beassociated with multiple entities, such as, for example, when an IPaddress is shared by an organization with many users behind it.Moreover, the specific method used to connect to the internet canobscure the identification of the entity 130 a-e. For example, an entity130 b may connect to the internet using an internet service provider(ISP) 200. Many ISPs 200 use dynamic host configuration protocol (DHCP)to assign IP addresses dynamically to entities 130 b requesting aconnection. Entities 130 a-e can also disguise their identity byspoofing a legitimate entity. Thus, collecting data on thecharacteristics of each entity 130 a-e can help to categorize an entity130 a-e and determine how to handle a communication.

The case of creation and spoofing of identities in both virtual andphysical world can create an incentive for users to act maliciouslywithout bearing the consequences of that act. For example, a stolen IPaddress on the Internet (or a stolen passport in the physical world) ofa legitimate entity by a criminal can enable that criminal toparticipate in malicious activity with relative ease by assuming thestolen identity. However, by assigning a reputation to the physical andvirtual entities and recognizing the multiple identities that they canemploy, reputation systems can influence reputable and non-reputableentities to operate responsibly for fear of becoming non-reputable, andbeing unable to correspond or interact with other network entities.

FIG. 3 is a block diagram depicting an example of communications andentities including using identifiers and attributes used to detectrelationships between entities. Security agents 100 a-b can collect databy examining communications that are directed to an associated network.Security agents 100 a-b can also collect data by examiningcommunications that are relayed by an associated network. Examinationand analysis of communications can allow the security agents 100 a-b tocollect information about the entities 300 a-c sending and receivingmessages, including transmission patterns, volume, or whether the entityhas a tendency to send certain kinds of message (e.g., legitimatemessages, spam, virus, bulk mail, etc.), among many others.

As shown in FIG. 3, each of the entities 300 a-c is associated with oneor more identifiers 310 a-c, respectively. The identifiers 310 a-c caninclude, for example, IP addresses, universal resource locator (URL),phone number, IM username, message content, domain, or any otheridentifier that might describe an entity. Moreover, the identifiers 310a-c are associated with one or more attributes 320 a-c. As should beunderstood, the attributes 320 a-c are fitted to the particularidentifier 310 a-c that is being described. For example, a messagecontent identifier could include attributes such as, for example,malware, volume, type of content, behavior, etc. Similarly, attributes320 a-c associated with an identifier, such as IP address, could includeone or more IP addresses associated with an entity 300 a-c.

Furthermore, it should be understood that this data can be collectedfrom communications 330 a-c (e.g., e-mail) typically include someidentifiers and attributes of the entity that originated thecommunication. Thus, the communications 330 a-c provide a transport forcommunicating information about the entity to the security agents 100 a,100 b. These attributes can be detected by the security agents 100 a,100 b through examination of the header information included in themessage, analysis of the content of the message, as well as throughaggregation of information previously collected by the security agents100 a, 100 b (e.g., totaling the volume of communications received froman entity).

The data from multiple security agents 100 a, 100 b can be aggregatedand mined. For example, the data can be aggregated and mined by acentral system which receives identifiers and attributes associated withall entities 300 a-c for which the security agents 100 a, 100 b havereceived communications. Alternatively, the security agents 100 a, 100 bcan operate as a distributed system, communicating identifier andattribute information about entities 300 a-c with each other. Theprocess of mining the data call correlate the attributes of entities 300a-c with each other, thereby determining relationships between entities300 a-c (such as, for example, correlations between an event occurrence,volume, and/or other determining factors).

These relationships can then be used to establish a multi-dimensionalreputation “vector” for all identifiers based on the correlation ofattributes that have been associated with each identifier. For example,if a non-reputable entity 300 a with a known reputation for beingnon-reputable sends a message 330 a with a first set of attributes 350a, and then an unknown entity 300 b sends a message 330 b with a secondset of attributes 350 b, the security agent 100 a can determine whetherall or a portion of the first set of attributes 350 a matched all or aportion of the second set of attributes 350 b. When some portion of thefirst set of attributes 350 a matches some portion of the second set ofattributes 330 b, a relationship can be created depending upon theparticular identifier 320 a, 3 b that included the matching attributes330 a, 330 b. The particular identifiers 340 a, 340 b which are found tohave matching attributes can be used to determine a strength associatedwith the relationship between the entities 300 a, 300 b. The strength ofthe relationship can help to determine how much of the non-reputablequalities of the non-reputable entity 300 a are attributed to thereputation of the unknown entity 300 b.

However, it should also be recognized that the unknown entity 300 b mayoriginate a communication 330 c which includes attributes 350 c thatmatch some attributes 350 d of a communication 330 d originating from aknown reputable entity 300 c. The particular identifiers 340 c, 340 dwhich are found to have matching attributes can be used to determine astrength associated with the relationship between the entities 300 b,300 c. The strength of the relationship can help to determine how muchof the reputable qualities of reputable entity 300 c are attributed tothe reputation of the unknown entity 300 b.

A distributed reputation engine also allows for real-time collaborativesharing of global intelligence about the latest threat landscape,providing instant protection benefits to the local analysis that can beperformed by a filtering or risk analysis system, as well as identifymalicious sources of potential new threats before they even occur. Usingsensors positioned at many different geographical locations informationabout new threats can be quickly and shared with the central system 200,or with the distributed security agents 100 a, 100 b. As should beunderstood, such distributed sensors can include the local securityagents 100 a, 100 b, as well as local reputation clients, trafficmonitors, or any other device suitable for collecting communication data(e.g., switches, routers, servers, etc.).

For examples security agents 100 a, 100 b can communicate with a centralsystem 200 to provide sharing of threat and reputation information.Alternatively, the security agents 100 a, 100 b can communicate threatand reputation information between each other to provide up to date andaccurate threat information. In the example of FIG. 3, the firstsecurity agent 100 a has information about the relationship between theunknown entity 300 b and the non-reputable entity 300 a, while thesecond security agent 100 b has information about the relationshipbetween the unknown entity 300 b and the reputable entity 300 c. Withoutsharing the information, the first security agent 100 a may take aparticular action on the communication based upon the detectedrelationship. However, with the knowledge of the relationship betweenthe unknown entity 300 b and the reputable entity 300 c, the firstsecurity agent 100 a might take a different action with a receivedcommunication from the unknown entity 300 b. Sharing of the relationshipinformation between security agents, thus provides for a more completeset of relationship information upon which a determination will be made.

The system attempts to assign reputations (reflecting a generaldisposition and/or categorization) to physical entities, such asindividuals or automated systems performing transactions. In the virtualworld, entities are represented by identifiers (ex. IPs, URLs, content)that are tied to those entities in the specific transactions (such assending a message or transferring money out of a bank account) that theentities are performing. Reputation can thus be assigned to thoseidentifiers based on their overall behavioral and historical patterns aswell as their relationship to other identifiers, such as therelationship of IPs sending messages and URLs included in thosemessages. A “bad” reputation for a single identifier can cause thereputation of other neighboring identifiers to worsen, if there is astrong correlation between the identifiers. For example, an IP that issending URLs which have a bad reputation will worsen its own reputationbecause of the reputation of the URLs. Finally, the individualidentifier reputations can be aggregated into a single reputation (riskscore) for the entity that is associated with those identifiers

It should be noted that attributes can fall into a number of categories.For example, evidentiary attributes can represent physical, digital, ordigitized physical data about an entity. This data can be attributed toa single known or unknown entity, or shared between multiple entities(forming entity relationships). Examples of evidentiary attributesrelevant to messaging security include IP (internet protocol) address,known domain names, URLs, digital fingerprints or signatures used by theentity, TCP signatures, and etcetera.

As another example, behavioral attributes can represent human ormachine-assigned observations about either an entity or an evidentiaryattribute. Such attributes may include one, many, or all attributes fromone or more behavioral profiles. For example, a behavioral attributegenerically associated with a spammer may by a high volume ofcommunications being sent from that entity.

A number of behavioral attributes for a particular type of behavior canbe combined to derive a behavioral profile. A behavioral profile cancontain a set of predefined behavioral attributes. The attributiveproperties assigned to these profiles include behavioral events relevantto defining the disposition of an entity matching the profile. Examplesof behavioral profiles relevant to messaging security might include,“Spammer”, “Scammer”, and “Legitimate Sender”. Events and/or evidentiaryattributes relevant to each profile define appropriate entities to whicha profile should be assigned. This may include a specific set of sendingpatterns, blacklist events, or specific attributes of the evidentiarydata. Some examples include: Sender/Receiver Identification; TimeInterval and sending patterns; Severity and disposition of payload;Message construction; Message quality; Protocols and related signatures;Communications medium

It should be understood that entities sharing some or all of the sameevidentiary attributes have an evidentiary relationship. Similarly,entities sharing behavioral attributes have a behavioral relationship.These relationships help form logical groups of related profiles, whichcan then be applied adaptively to enhance the profile or identifyentities slightly more or less standard with the profiles assigned.

FIG. 4 is a flowchart depicting an operational scenario 400 used todetect relationships and assign risk to entities. The operationalscenario begins at step 410 by collecting network data. Data collectioncan be done, for example, by a security agent 100, a client device, aswitch, a router, or any other device operable to receive communicationsfrom network entities (e.g., e-mail servers, web servers, IM servers,ISPs, file transfer protocol (FTP) servers, gopher servers, VoIPequipments, etc.).

At step 420 identifiers are associated with the collected data (e.g.,communication data). Step 420 can be performed by a security agent 100or by a central system 200 operable to aggregate data from a number ofsensor devices including, for example, one or more security agents 100.Alternatively, step 420 can be performed by the security agents 100themselves. The identifiers can be based upon the type of communicationreceived. For example, an e-mail can include one set of information(e.g., IP address of originator and destination, text content,attachment, etc.), while a VoIP communication can include a differentset of information (e.g., originating phone number (or IP address iforiginating from a VoIP client), receiving phone number (or IP addressif destined for a VoIP phone), voice content, etc.). Step 420 can alsoinclude assigning the attributes of the communication with theassociated identifiers.

At step 430 the attributes associated with the entities are analyzed todetermine whether any relationships exist between entities for whichcommunications information has been collected. Step 430 can beperformed, for example, by a central system 200 or one or moredistributed security agents 100. The analysis can include comparingattributes related to different entities to find relationships betweenthe entities. Moreover, based upon the particular attribute which servesas the basis for the relationship, a strength can be associated with therelationship.

At step 440 a risk vector is assigned to the entities. As an example,the risk vector can be assigned by the central system 200 or by one ormore security agents 100. The risk vector assigned to an entity 130(FIGS. 1-2), 300 (FIG. 3) can be based upon the relationship foundbetween the entities and on the basis of the identifier which formed thebasis for the relationship.

At step 450, an action can be performed based upon the risk vector. Theaction can be performed, for example, by a security agent 100. Theaction can be performed on a received communication associated with allentity for which a risk vector has been assigned. The action can includeany of allow, deny, quarantine, load balance, deliver with assignedpriority, or analyze locally with additional scrutiny, among manyothers. However, it should be understood that a reputation vector can bederived separately

FIG. 5 is a block diagram illustrating an example network architectureincluding local reputations 500 a-e derived by local reputation engines510 a-e and a global reputation 520 stored by one or more servers 530.The local reputation engines 510 a-e, for example, can be associatedwith local security agents such as security agents 100. Alternatively,the local reputation engines 510 a-e can be associated, for example,with a local client. Each of the reputation engines 510 a-e includes alist of one or more entities for which the reputation engine 510 a-estores a derived reputation 500 a-e.

However, these stored derived reputations can be inconsistent betweenreputation engines, because each of the reputation engines may observedifferent types of traffic. For example, reputation engine 1 510 a mayinclude a reputation that indicates a particular entity is reputable,while reputation engine 2 510 b may include a reputation that indicatesthat the same entity is non-reputable. These local reputationalinconsistencies can be based upon different traffic received from theentity. Alternatively, the inconsistencies can be based upon thefeedback from a user of local reputation engine 1 510 a indicating acommunication is legitimate, while a user of local reputation engine 2510 b provides feedback indicating that the same communication is notlegitimate.

The server 530 receives reputation information from the local reputationengines 510 a-e. However, as noted above, some of the local reputationinformation may be inconsistent with other local reputation information.The server 530 can arbitrate between the local reputations 500 a-e todetermine a global reputation 520 based upon the local reputationinformation 500 a-e. In some examples, the global reputation information520 can then be provided back to the local reputation engines 510 a-e toprovide these local engines 500 a-e with up-to-date reptutationalinformation. Alternative, the local reputation engines 510 a-e can beoperable to query the server 530 for reputation information. In someexamples, the server 530 responds to the query with global reputationinformation 520.

In other examples, the server 530 applies a local reputation bias to theglobal reputation 520. The local reputation bias can perform a transformon the global reputation to provide the local reputation engines 510 a-ewith a global reputation vector that is biased based upon thepreferences of the particular local reputation engine 510 a-e whichoriginated the query Thus, a local reputation engine 510 a with anadministrator or user(s) that has indicated a High tolerance for spammessages can receive a global reputation vector that accounts for anindicated tolerance. The particular components of the reputation vectorreturns to the reputation engine 510 a might include portions of thereputation vector that are deemphasized with relationship to the rest ofthe reputation vector. Likewise, a local reputation engine 510 b thathas indicated, for example, a low tolerance communications from entitieswith reputations for originating viruses may receive a reputation vectorthat amplifies the components of the reputation vector that relate tovirus reputation.

FIG. 6 is a block diagram illustrating a determination of a globalreputation based on local reputation feedback. A local reputation engine600 is operable to send a query through a network 610 to a server 620.In some examples, the local reputation engine 600 originates a query inresponse to receiving a communication from an unknown entityAlternatively the local reputation engine 600 can originate the queryresponsive to receiving any communications, thereby promoting use ofmore up-to-date reputation information.

The server 620 is operable to respond to the query with a globalreputation determination. The central server 620 can derive the globalreputation using a global reputation aggregation engine 630. The globalreputation aggregation engine 630 is operable to receive a plurality oflocal reputations 640 from a respective plurality of local reputationengines. In some examples, the plurality of local reputations 640 can beperiodically sent by the reputation engines to the server 620.Alternatively, the plurality of local reputations 640 can be retrievedby the server upon receiving a query from one of the local reputationengines 600.

The local reputations can be combined using confidence values related toeach of the local reputation engines and then accumulating the results.The confidence value can indicate the confidence associated with a localreputation produced by an associated reputation engine. Reputationengines associated with individuals, for example, can receive a lowerweighting in the global reputation determination. In contrast, localreputations associated with reputation engines operating on largenetworks can receive greater weight in the global reputationdetermination based upon the confidence value associated with thatreputation engine.

In some examples, the confidence values 650 can be based upon feedbackreceived from users. For example, a reputation engine that receives alot of feedback indicating that communications were not properly handledbecause local reputation information 640 associated with thecommunication indicated the wrong action can be assigned low confidencevalues 650 for local reputations 640 associated with those reputationengines. Similarly, reputation engines that receive feedback indicatingthat the communications were handled correctly based upon localreputation information 640 associated with the communication indicatedthe correct action can be assigned a high confidence value 650 for localreputations 640 associated with the reputation engine. Adjustment of theconfidence values associated with the various reputation engines can beaccomplished using a tuner 660, which is operable to receive inputinformation and to adjust the confidence values based upon the receivedinput. In some examples, the confidence values 650 can be provided tothe server 620 by the reputation engine itself based upon storedstatistics for incorrectly classified entities. In other examples,information used to weight the local reputation information can becommunicated to the server 620.

In some examples, a bias 670 can be applied to the resulting globalreputation vector. The bias 670 can normalize the reputation vector toprovide a normalized global reputation vector to a reputation engine600. Alternatively, the bias 670 can be applied to account for localpreferences associated with the reputation engine 600 originating thereputation query. Thus, a reputation engine 600 can receive a globalreputation vector matching the defined preferences of the queryingreputation engine 600. The reputation engine 600 can take an action onthe communication based upon the global reputation vector received fromthe server 620.

FIG. 7 is a block diagram illustrating an example resolution between aglobal reputation and a local reputation. The local security agent 700communicates with a server 720 to retrieve global reputation informationfrom the server 720. The local security agent 700 can receive acommunication at 702. The local security agent can correlate thecommunication to identify attributes of the message at 704. Theattributes of the message can include, for example, an originatingentity, a fingerprint of the message content, a message size, etc. Thelocal security agent 700 includes this information in a query to theserver 720. In other examples, the local security agent 700 can forwardthe entire message to the server 720, and the server can perform thecorrelation and analysis of the message.

The server 720 uses the information received from the query to determinea global reputation based upon a configuration 725 of the server 720.The configuration 725 can include a plurality of reputation information,including both information indicating that a queried entity isnon-reputable 730 and information indicating that a queried entity isreputable 735. The configuration 725 can also apply a weighting 740 toeach of the aggregated reputations 730, 735. A reputation scoredeterminator 745 can provide the engine for weighting 740 the aggregatedreputation information 730, 735 and producing a global reputationvector.

The local security agent 700 then sends a query to a local reputationengine at 706. The local reputation engine 708 performs a determinationof the local reputation and returns a local reputation vector at 710.The local security agent 700 also receives a response to the reputationquery sent to the server 720 in the form of a global reputation vector.The local security agent 700 then mixes the local and global reputationvectors together at 712. An action is then taken with respect to thereceived message at 714.

FIG. 8 is an example graphical user interface 800 for adjusting thesettings of a filter associated with a reputation server. The graphicaluser interface 800 can allow the user of a local security agent toadjust the settings of a local filter in several different categories810, such as, for example, “Virus,” “Worms,” “Trojan Horse,” “Phishing,”“Spyware,” “Spam,” “Content,” and “Bulk.” However, it should beunderstood that the categories 810 depicted are merely examples, andthat the disclosure is not limited to the categories 810 chosen asexamples here.

In some examples, the categories 810 can be divided into two or moretypes of categories, For example, the categories 810 of FIG. 8 aredivided into a “Security Settings” type 820 of category 810, and a“Policy Settings” type 830 of category. In each of the categories 810and types 820, 830, a mixer bar representation 840 can allow the user toadjust the particular filter setting associated with the respectivecategory 810 of communications or entity reputations.

Moreover, while categories 810 of “Policy Settings” type 830 call beadjusted freely based upon the user's own judgment, categories of“Security Settings” type 820 can be limited to adjustment within arange. This distinction can be made in order to prevent a user frontaltering the security settings of the security agent beyond anacceptable range. For example, a disgruntled employee could attempt tolower the security settings, thereby leaving an enterprise networkvulnerable to attack. Thus, the ranges 850 placed on categories 810 inthe “Security Settings” type 820 are operable to keep security at aminimum level to prevent the network from being compromised. However, asshould be noted, the “Policy Settings” type 830 categories 810 are thosetypes of categories 810 that would not compromise the security of anetwork, but might only inconvenience the user or the enterprise if thesettings were lowered.

Furthermore, it should be recognized that in various examples, rangelimits 850 can be placed upon all of the categories 810. Thus, the localsecurity agent would prevent users from setting the mixer barrepresentation 840 outside of the provided range 850. It should also benoted, that in some examples, the ranges may not be shown on thegraphical user interface 800. Instead, the range 850 would be abstractedout of the graphical user interface 800 and all of the settings would berelative settings. Thus, the category 810 could display and appear toallow a full range of settings, while transforming the setting into asetting within the provided range. For example, the “Virus” category 810range 850 is provided in this example as being between level markers 8and 13. If the graphical user interface 800 were set to abstract theallowable range 850 out of the graphical user interface 800, the “Virus”category 810 would allow setting of the mixer bar representation 840anywhere between 0 and 14. However, the graphical user interface 800could transform the 0-14 setting to a setting within the 8 to 13 range850. Thus, if a user requested a setting of midway between 0 and 14, thegraphical user interface could transform that setting into a setting ofmidway between 8 and 13.

FIG. 9 is a block diagram illustrating reputation based connectionthrottling for voice over internet protocol (VoIP) or short messageservice (SMS) communications. As should be understood, an originating IPphone 900 can place a VoIP call to a receiving IP phone 910. These IPphones 900, 910 can be, for example, computers executing soft-phonesoftware, network enabled phones, etc. The originating IP phone 900 canplace a VoIP call through a network 920 (e.g., the internet). Thereceiving IP phone 910 can receive the VoIP call through a local network930 (e.g., an enterprise network).

Upon establishing a VoIP call, the originating IP phone has establisheda connection to the local network 930. This connection can be exploitedsimilarly to the way e-mail, web, instant messaging, or other internetapplications can be exploited for providing unregulated connect to anetwork. Thus, a connection to a receiving IP phone can be exploited,thereby putting computers 940, 950 operating on the local network 930 atrisk for intrusion, viruses, trojan horses, worms, and various othertypes of attacks based upon the established connection. Moreover,because of the time sensitive nature of VoIP communications, thesecommunications are typically not examined to ensure that the connectionis not being misused. For example, voice conversations occur inreal-time. If a few packets of a voice conversation are delayed, theconversation becomes stilted and difficult to understand. Thus, thecontents of the packets typically cannot be examined once a connectionis established.

However, a local security agent 960 can use reputation informationreceived from a reputation engine or server 970 to determine areputation associated with the originating IP phone. The local securityagent 960 can use the reputation of the originating entity to determinewhether to allow a connection to the originating entity. Thus, thesecurity agent 960 can prevent connections to non-reputable entities, asindicated by reputations that do not comply with the policy of the localsecurity agent 960.

In some examples, the local security agent 960 can include a connectionthrottling engine operable to control the flow rate of packets beingtransmitted using the connection established between the originating IPphone 900 and the receiving IP phone 910. Thus, an originating entities900 with a non-reputable reputation can be allowed to make a connectionto the receiving IP phone 910. However, the packet throughput will becapped, thereby preventing the originating entity 900 from exploitingthe connection to attack the local network 930. Alternatively, thethrottling of the connection can be accomplished by performing adetailed inspection of any packets originating from non-reputableentities. As discussed above, the detailed inspection of all VoIPpackets is not efficient. Thus, quality of service (QoS) can bemaximized for connections associated with reputable entities, whilereducing the QoS associated with connections to non-reputable entities.Standard communication interrogation techniques can be performed onconnections associated with non-reputable entities in order to discoverwhether any of the transmitted packets received from the originatingentity comprise a threat to the network 930. Various interrogationtechniques and systems are described in U.S. Pat. No. 6,941,467, No.7,089,590, No. 7,096,498, and No. 7,124,438 and in U.S. PatentApplication Nos. 2006/00015942, 2006/0015563, 9003/0172302,2003/0172294, 2003/0172291, and 2003/0172166, which are herebyincorporated by reference.

FIG. 10 is a block diagram illustrating an operation of a reputationbased load balancer 1000. The load balancer 1000 is operable to receivecommunications from reputable and non-reputable entities 1010, 1020(respectively) through a network 1030 (e.g., the internet). The loadbalancer 1000 communicates with a reputation engine 1040 to determinethe reputation of entities 1010, 1020 associated with incoming oroutgoing communications.

The reputation engine 1030 is operable to provide the load balancer witha reputation vector. The reputation vector can indicate the reputationof the entity 1010, 1020 associated with the communication in a varietyof different categories. For example, the reputation vector mightindicate a good reputation for an entity 1010, 1020 with respect to theentity 1010, 1020 originating spam, while also indicating a poorreputation for the same entity 1010, 1020 with respect to that entity1010, 1020 originating viruses.

The load balancer 1000 can use the reputation vector to determine whataction to perform with respect to a communication associated with thatentity 1010, 1020. In situations where a reputable entity 1010 isassociated with the communication, the message is sent to a messagetransfer agent (MTA) 1050 and delivered to a recipient 1060.

In situations where a non-reputable entity 1020 has a reputation forviruses, but does not have a reputation for other types of non-reputableactivity, the communication is forwarded to one of a plurality of virusdetectors 1070. The load balancer 1000 is operable to determine which ofthe plurality of virus detectors 1070 to use based upon the currentcapacity of the virus detectors and the reputation of the originatingentity. For example, the load balancer 1000 could send the communicationto the least utilized virus detector. In other examples, the loadbalancer 1000 might determine a degree of non-reputability associatedwith the originating entity and send slightly non-reputablecommunications to the least utilized virus detectors, while sendinghighly non-reputable communications to a highly utilized virus detector,thereby throttling the QoS of a connection associated with a highlynon-reputable entity.

Similarly, in situations where a non-reputable entity 1020 has areputation for originating spam communications, but no other types ofnon-reputable activities, the load balancer can send the communicationto specialized spam detectors 1080 to the exclusion of other types oftesting. It should be understood that in situations where acommunication is associated with a non-reputable entity 1020 thatoriginates multiple types of non-reputable activity, the communicationcan be sent to be tested for each of the types of non-reputable activitythat the entity 1020 is known to display, while avoiding testsassociated with non-reputable activity that the entity 1020 is not knownto display.

In some examples, every communication can receive routine testing formultiple types of non-legitimate content, However, when an entity 1020associated with the communication shows a reputation for certain typesof activity, the communication can also be quarantined for detailedtesting for the content that the entity shows a reputation fororiginating.

In yet further examples, every communication may receive the same typeof testing. However, communications associated with reputable entities1010 is sent to the testing modules with the shortest queue or totesting modules with spare processing capacity. On the other hand,communications associated with non-reputable entities 1020 is sent totesting modules 1070, 1080 with the longest queue. Therefore,communications associated with reputable entities 1010 can receivepriority in delivery over communications associated with non-reputableentities. Quality of service is therefore maximized for reputableentities 1010, while being reduced for non-reputable entities 1020.Thus, reputation based load balancing can protect the network fromexposure to attack by reducing the ability of a non-reputable entity toconnect to the network 930.

FIG. 11A is a flowchart illustrating an example operational scenario forcollection of geolocation based data for authentication analysis. Atstep 1100 the operational scenario collects data from various loginattempts. Step 1100 can be performed for example by a local securityagent, such as the security agent 100 of FIG. 1. The collected data caninclude IP address associated with the login attempt, time of the loginattempt, number of login attempts before successful, or the details ofany unsuccessful passwords attempted, among many other types ofinformation. The collected data is then analyzed in step 1105 to derivestatistical information such as, for example, a geographical location ofthe login attempts. Step 1105 can be performed, for example, by areputation engine. The statistical information associated with the loginattempts is then stored at step 1110. The storing can be performed, forexample, by a system data store.

FIG. 11B is a flowchart illustrating an example operational scenario forgeolocation based authentication. A login attempt is received at step1115. The login attempt can be received for example, by a secure webserver operable to provide secure financial data over a network. It isthen determined whether the login attempt matches a stored username andpassword combination at step 1120. Step 1120 can be performed, forexample, by a secure server operable to authenticate login attempts. Ifthe username and password do not match a stored username/passwordcombination, the login attempt is declared a failure at step 1125.

However, if the username and password do match a legitimateusername/password combination, the origin of the login attempt isascertained at step 1130. The origin of the login attempt can bedetermined by a local security agent 100 as described in FIG. 1.Alternatively, the origin of the login attempt can be determined by areputation engine. The origin of the login attempt can then be comparedwith the statistical information derived in FIG. 11A, as shown in step1135. Step 1135 can be performed, for example, by a local security agent100 or by a reputation engine. It is determined whether the originmatches statistical expectations at step 1140. If the actual originmatches statistical expectations, the user is authenticated at step1145.

Alternatively, if the actual origin does not match statisticalexpectations for the origin, further processing is performed in step1150. It should be understood that further processing can includerequesting further information from the user to verify his or herauthenticity. Such information can include, for example, home address,mother's maiden name, place of birth, or any other piece of informationknown about the user (e.g., secret question). Other examples ofadditional processing can include searching previous login attempts todetermine whether the location of the current login attempt is trulyanomalous or merely coincidental. Furthermore, a reputation associatedwith the entity originating the login attempt can be derived and used todetermine whether to allow the login.

FIG. 11C is a flowchart illustrating another example operationalscenario for geolocation based authentication using reputation of anoriginating entity to confirm authentication. A login attempt isreceived at step 1155. The login attempt can be received for example, bya secure web server operable to provide secure financial data over anetwork. It is then determined whether the login attempt matches astored username and password combination at step 1160. Step 1160 can beperformed, for example, by a secure server operable to authenticatelogin attempts. If the username and password do not match a storedusername/password combination, the login attempt is declared a failureat step 1165.

However, if the username and password do match a legitimateusername/password combination, the origin of the login attempt isascertained at step 1170. The origin of the login attempt can bedetermined by a local security agent 100 as described in FIG. 1.Alternatively, the origin of the login attempt can be determined by areputation engine. A reputation associated with the entity originatingthe login attempt can then be retrieved, as shown in step 1175. Step1175 can be performed, for example, by a reputation engine. It isdetermined whether the reputation of the originating entity is reputableat step 1180. If the originating entity is reputable, the user isauthenticated at step 1185.

Alternatively, if the originating entity is non-reputable, furtherprocessing is performed in step 1190. It should be understood thatfurther processing can include requesting further information from theuser to verify his or her authenticity. Such information can include,for example, home address, mother's maiden name, place of birth, or anyother piece of information known about the user (e.g., secret question).Other examples of additional processing can include searching previouslogin attempts to determine whether the location of the current loginattempt is truly anomalous or merely coincidental.

Thus, it should be understood that reputation systems can be applied toidentifying fraud in financial transactions. The reputation system canraise the risk score of a transaction depending on the reputation of thetransaction originator or the data in the actual transaction (source,destination, amount, etc). In such situations, the financial institutioncan better determine the probability that a particular transaction isfraudulent based upon the reputation of the originating entity.

FIG. 12 is a flowchart illustrating an example operational scenario fora reputation based dynamic quarantine. Communications are received atstep 1200. The communications are then analyzed to determine whetherthey are associated with an unknown entity at step 1205. It should benoted, however, that this operational scenario could be applied to anycommunications received, not merely communications received frompreviously unknown entities. For example, communications received from anon-reputable entity could be dynamically quarantined until it isdetermined that the received communications do no pose a threat to thenetwork. Where the communications are not associated with a new entity,the communications undergo normal processing for incoming communicationsas shown in step 1210.

If the communications are associated with a new entity, a dynamicquarantine counter is initialized in step 1215. Communications receivedfrom the new entity are then sent to a dynamic quarantined at step 1220.The counter is then checked to determine whether the counter has elapsedin step 1225. If the counter has not elapsed, the counter is decrementedin step 1230. The behavior of the entity as well as the quarantinedcommunications can be analyzed in step 1235. A determination is madewhether the quarantined communications or behavior of the entity isanomalous in step 1240. If there is no anomaly found, the operationalscenario returns to step 1220, where new communications are quarantined.

However, if the communications or behavior of the entity are found to beanomalous in step 1240, a non-reputable reputation is assigned to theentity in step 1245. The process ends by sending notification to anadministrator or recipients of communications sent by the originatingentity.

Returning to step 1220, the process of quarantining and examining,communications and entity behavior continues until anomalous behavior isdiscovered, or until the dynamic quarantine counter elapses in step1225. If the dynamic quarantine counter elapses, a reputation isassigned to the entity at step 1255. Alternatively, in situations wherethe entity is not an unknown entity, the reputation would be updated insteps 1245 or 1255. The operational scenario ends at step 1260 byreleasing the dynamic quarantine where the dynamic quarantine counterhas elapsed without discovery of an anomaly in the communications or inthe originating entity behavior.

FIG. 13 is an example graphical user interface 1300 display of an imagespam communication which can be classified as an unwanted image ormessage. As should be understood, image spam poses a problem fortraditional spam filters. Image spam bypasses the traditional textualanalysis of spam by converting the text message of the spam into animage format. FIG. 13 shows an example of image spam. The message showsan image 1310. While the image 1300 appears to be textual, it is merelythe graphic encoding of a textual message. Image spam also typicallyincludes a textual message 1320 comprising sentences which arestructured correctly, but make no sense in the context of the message.The message 1320 is designed to elude spam filters that key oncommunications that only include an image 1310 within the communication.Moreover, the message 1320 is designed to trick filters that applysuperficial testing to the text of a communication that includes animage 1310. Further, while these messages do include information aboutthe origination of the message in the header 1330, an entity'sreputation for originating image spam might not be known until theentity is caught sending image spam.

FIG. 14 is a flowchart illustrating an example operational scenario fordetecting unwanted images (e.g., image spam). It should be understoodthat many of the steps shown in FIG. 14 can be performed alone or incombination with any or all of the other steps shown in FIG. 14 toprovide some detection of image spam. However, the use of each of thesteps in FIG. 14 provides a comprehensive process for detecting imagespam.

The process begins at step 1400 with analysis of the communication. Step1400 typically includes analyzing the communication to determine whetherthe communication includes an image that is subject to image spamprocessing. At step 1410, the operational scenario performs a structuralanalysis of the communication to determine whether the image comprisesspam. The header of the image is then analyzed in step 1420. Analysis ofthe image header allows the system to determine whether anomalies existwith respect to the image format itself (e.g., protocol errors,corruption, etc.). The features of the image are analyzed in step 1430.The feature analysis is intended to determine whether any of thefeatures of the image are anomalous.

The image can be normalized in step 1440. Normalization of an imagetypically includes removal of random noise that might be added by aspammer to avoid image fingerprinting techniques. Image normalization isintended to convert the image into a format that can be easily comparedamong images. A fingerprint analysis can be performed on the normalizedimage to determine whether the image matches images from previouslyreceived known image spam.

FIG. 15A is a flowchart illustrating an operational scenario foranalyzing the structure of a communication. The operational scenariobegins at step 1500 with analysis of the message structure. At step 1505the hypertext mark-up language (HTML) structure of the communication isanalyzed to introduce n-gram tags as additional tokens to a Bayesiananalysis. Such processing can analyze the text 1320 that is included inan image spam communication for anomalies. The HTML, structure of themessage can be analyzed to define meta-tokens. Meta-tokens are the HTMLcontent of the message, processed to discard any irrelevant HTML tagsand compressed by removing white space to create a “token” for Bayesiananalysis. Each of tile above described tokens can be used as input to aBayesian analysis for comparison to previously received communications.

The operational scenario then includes image detection at step 1515. Theimage detection can include partitioning the image into a plurality ofpieces and performing fingerprinting on the pieces to determine whetherthe fingerprints match pieces of previously received images.

FIG. 15B is a flowchart illustrating an operational scenario foranalyzing the features of an image to extract features of the messagefor input into a clustering engine to identify components of the imagewhich align with known image spam. The operational scenario begins atstep 1520 where a number of high level features of the image aredetected for use in a machine learning algorithm. Such features caninclude values such as the number of unique colors, number of noiseblack pixels, number of edges in horizontal direction (sharp transitionsbetween shapes), etc.

One of the features extracted by the operational scenario can includethe number of histogram modes of the image, as show at step 1525. Thenumber of modes is yielded by an examination of spectral intensity ofthe image. As should be understood, artificial images will typicallyinclude fewer modes than natural images, because natural image colorsare typically spread through a broad spectrum.

As described above, the features extracted from the image can be used toidentify anomalies. In some examples, anomalies can include analyzingthe characteristics of a message to determine a level of similarity of anumber of features to the features of stored unwanted images.Alternatively, in some examples, the image features can also be analyzedfor comparison with known reputable images to determine similarity toreputable images. It should be understood that none of the extractedfeatures alone are determinative of a classification. For example, aspecific feature might be associated with 60% of unwanted messages,while also being associated with 40% of wanted messages. Moreover, asthe value associated with the feature changed, there might be a changein the probability that the message is wanted or unwanted. There aremany features that can indicate a slight tendency. If each of thesefeatures are combined the image spam detection system can makeclassification decision.

The aspect ratio is then examined in step 1530 to determine whetherthere are any anomalies with respect to the image size or aspect. Suchanomalies in the aspect ratio could be indicated by similarity of theimage size or aspect ratio to known sizes or aspect ratios which arecommon to known image spam. For example, image spam can come in specificsizes to make the image spam look more like common e-mail. Messages thatinclude images which share a common size with known spam images are morelikely to be spam themselves. Alternatively, there are image sizes whichare not conducive to spam (e.g., a 1″×1″ square image might be difficultto read if a spammer inserted a message into the image). Messages thatinclude images which are known to be non-conducive to spam insertion areless likely to be image spam. Thus, the aspect ratio of a message can becompared to common aspect ratios used in image spam to determine aprobability that the image is an unwanted image or that the image is areputable image.

At step 1535, the frequency distribution of the image is examined.Typically, natural pictures have uniform frequency distribution with arelative scarcity of sharp frequency gradations. On the other hand,image spam typically includes a choppy frequency distribution as aresult of black letters being placed on a dark: background. Thus, suchnon-uniform frequency distribution can indicate image spam.

At step 1540, the signal to noise ratio can be analyzed. A high signalto noise ratio might indicate that a spammer may be trying to evadefingerprinting techniques by introducing noise into the image.Increasing noise levels can thereby indicate an increasing probabilitythat the image is an unwanted image.

It should be understood that some features can be extracted on the scaleof the entire image, while other features can be extracted from subpartsof the image. For example, the image can be subdivided into a pluralityof subparts. Each of the rectangles can be transformed into a frequencydomain using a fast Fourier transform (FFT). In the transformed image,the predominance of frequencies in a plurality of directions can beextracted as features. These subparts of the transformed image can alsobe examined to determine the amount of high frequencies and lowfrequencies. In the transformed image, the points that are further awayfrom the origin represent higher frequencies. Similarly to the otherextracted features, these features can then be compared to knownlegitimate and unwanted images to determine which characteristics theunknown image shares with each type of known image. Moreover, thetransformed (e.g., frequency domain) image can also be divided intosubparts (e.g., slices, rectangles, concentric circles, etc.) andcompared against data from known images (e.g., both known unwantedimages and known legitimate images).

FIG. 15C is a flowchart illustrating an operational scenario fornormalizing the an image for spam processing. At step 1545, obfuscationand noise is removed from the image. As discussed previously, these canbe introduced by spammers to evade fingerprinting techniques such ashashing by varying the sum of the hash such that it does not match anypreviously received hash fingerprints of known image spam. Obfuscationand noise removal can describe several techniques for removingartificial noise introduced by spammers. It should be understood thatartificial noise can include techniques used by spammers such as banding(where a font included in the image is varied to vary the hash of theimage).

An edge detection algorithm can be run on the normalized image at step1550. In some examples, the edge detected image can be used provided toan optical character recognition engine to convert the edge detectedimage to text. The edge detection can be used to remove unnecessarydetail from the picture which can cause inefficiency in processing theimage again other images.

At step 1555, median filtering can be applied. The median filtering isapplied to remove random pixel noise. Such random pixels can causeproblems to content analysis of the image. The median filtering can helpto remove single pixel type of noise introduced by spammers. It shouldbe understood that single pixel noise is introduced by spammers using animage editor to alter one or more pixels in the image, which can makethe image appear grainy in some areas, thereby making the image moredifficult to detect.

At step 1560, the image is quantized. Quantizing of the image removeunnecessary color information. The color information typically requiresmore processing and is unrelated to the attempted propagation of thespam. Moreover, spammers could vary the color scheme in an imageslightly and again vary the hash such that known image spam hashes wouldnot match the derived hash from the color variant image spam.

At step 1565, contrast stretching is performed. Using contraststretching the color scale in the image is maximized from black towhite, even if the colors only vary through shades of gray. The lightestshade of the image is assigned a white value, while the darkest shade inthe image is assigned a black value. All other shades are assigned theirrelative position in the spectrum in comparison to the lightest anddarkest shades in the original image. Contrast stretching helps todefine details in an image that may not make full use of the availablespectrum and therefore can help to prevent spammers from using differentpieces of the spectrum to avoid fingerprinting techniques. Spammerssometimes intentionally shift the intensity range of an image to defeatsome types of feature identification engines. Contrast stretching canalso help normalize an image such that it can be compared to otherimages to identify common features contained in the images.

FIG. 15D is a flowchart illustrating an operational scenario foranalyzing the fingerprint of an image to find common fragments amongmultiple images. The operational scenario begins a step 1570 by definingregions within an image. A winnowing algorithm is then performed on thedefined regions to identify the relevant portions of the image uponwhich fingerprints should be taken at step 1575. At step 1580, theoperational scenario fingerprints the resulting fragments from thewinnowing operation and determines whether there is a match between thefingerprints of the received image an known spam images. A similarwinnowing fingerprint approach is described in U.S. Patent ApplicationPublication No. 2006/025068, which is hereby incorporated by reference.

As used in the description herein and throughout the claims that follow,the meaning of “a,” “an,” and “the” includes plural reference unless thecontext clearly dictates otherwise. Also, as used in the descriptionherein and throughout the claims that follow, the meaning of “in”includes “in” and “on” unless the context clearly dictates otherwise.Finally, as used in the description herein and throughout the claimsthat follow, the meanings of “and” and “or” include both the conjunctiveand disjunctive and may be used interchangeably unless the contextclearly dictates otherwise.

Ranges may be expressed herein as from “about” one particular value,and/or to “about” another particular value. When such a range isexpressed, another embodiment includes from the one particular valueand/or to the other particular value. Similarly, when values areexpressed as approximations, by use of the antecedent “about,” it willbe understood that the particular value forms another embodiment. Itwill be further understood that the endpoints of each of the ranges aresignificant both in relation to the other endpoint, and independently ofthe other endpoint.

A number of embodiments of the invention have been described.Nevertheless, it will be understood that various modifications may bemade without departing from the spirit and scope of the invention.Accordingly, other embodiments are within the scope of the following,claims.

1. A computer-implemented reputation system, the system comprising: acentralized reputation engine operable to receive feedback from aplurality of local reputation engines, the plurality of local reputationengines being operable to determine local reputations based upon of oneor more entities and respectively associated with the local reputationengines; an aggregation engine operable to derive a global reputationfor a queried entity based upon an aggregation of the plurality of localreputations; and wherein; the centralized reputation engine is operableto provide the global reputation of the queried entity to one or more ofthe local reputation engines responsive to receiving a reputation queryfrom said one or more of the local reputation engines; the centralizedreputation engine is operable to apply, for each local reputation enginethat originates a reputation query, a local reputation bias to theglobal reputation based on preferences of the local reputation engine togenerate a local reputation for the local reputation engine from theglobal reputation, so that for each local reputation engine a differentlocal reputation is generated from the global reputation.
 2. The systemof claim 1, wherein the aggregation engine is operable to storeconfidence values associated with a respective local reputation engine,the aggregation engine being further operable to aggregate the pluralityof local reputations using the confidence values associated with each ofthe plurality of local reputations through its respective localreputation engine.
 3. The system of claim 2, wherein each localreputation engine is a subsystem to the centralized reputation system,and performs the reputation scoring on a local scale based upon thecommunications received by the local reputation engine, and thecentralized reputation engine performs reputation scoring based uponcommunications received by the centralized reputation engine andreputation information received from the local reputation engines. 4.The system of claim 2, wherein the local reputations are weighted basedon their respective confidence values prior to aggregation of the localreputations.
 5. The system of claim 4, wherein the confidence values aretuned based upon feedback received from the plurality of localreputation engines.
 6. The system of claim 1, wherein the localreputations and global reputations are vectors which identify thecharacteristics of the respective entities to which they are associated.7. The system of claim 6, wherein the characteristics comprise one ormore of a spamming characteristic, a phishing characteristic, a bulkmailing characteristic, a virus source characteristic, a legitimatecommunication characteristic, an intrusion characteristic, an attackcharacteristic, a spyware characteristic, or a geolocationcharacteristic.
 8. The system of claim 1, wherein the local reputationsare based upon an aggregation of reputable and non-reputable criteria.9. The system of claim 1, wherein generating a local reputation for thelocal reputation engine from the global reputation comprises emphasizingfirst criteria for reputation in the global reputation whilede-emphasizing second other criteria for reputation in the globalreputation based upon the local reputation bias.
 10. The system of claim1, wherein the local reputation engine originates the reputation queryresponsive to receiving a communication associated with an externalentity with respect to a protected enterprise network associated withthe local reputation engine.
 11. The system of claim 10, wherein thelocal reputation engine originates the reputation query responsive to alocal reputation associated with the external entity beingindeterminate.
 12. The system of claim 1, wherein the centralizedreputation engine is further operable to aggregate reputation for aplurality of identities associated with one or more of the plurality ofentities.
 13. The system of claim 12, wherein the centralized reputationengine is further operable to correlate attributes associated withdifferent identities to identify relationships between the differententities, and to assign a portion of the reputation associated with oneentity to the reputation of another entity where a relationship havebeen identified between entities.
 14. A computer-implemented method ofproducing a global reputation, comprising the steps of: receiving areputation query from a requesting local reputation engine; retrieving aplurality of local reputations the local reputations being respectivelyassociated with a plurality of local reputation engines; aggregating theplurality of local reputations; deriving a global reputation from theaggregation of the local reputations; applying, for each localreputation engine that originates a reputation query, a local reputationbias to the global reputation based on preferences of the localreputation engine to generate a local reputation for the localreputation engine from the global reputation, so that for each localreputation engine a different local reputation is generated from theglobal reputation; and responding to the reputation query with theglobal reputation.
 15. The method of claim 14, further comprisingretrieving confidence values associated with the local reputationengines, the deriving step using the confidence values to derive theglobal reputation.
 16. The method of claim 15, wherein the deriving stepfurther comprises weighting the local reputations using their respectiveconfidence values and combining the weighted reputations to generate theglobal reputation.
 17. The method of claim 16, further comprising tuningthe confidence values based upon feedback from the plurality of localreputation engines.
 18. The method of claim 14, wherein the localreputations and global reputations are vectors which identify thecharacteristics of the respective entities to which they are associated.19. The method of claim 18, wherein the characteristics comprise one ormore of a spamming characteristic, a phishing characteristic, a bulkmailing characteristic, a malware source characteristic, or a legitimatemail characteristic.
 20. The method of claim 14, wherein the localreputations are based upon an aggregation of reputable and non-reputablecriteria.
 21. The method of claim 14, wherein generating a localreputation for the local reputation engine from the global reputationcomprises emphasizing first criteria for reputation in the globalreputation based upon the local reputation bias and de-emphasizingsecond other criteria for reputation in the global reputation based uponthe local reputation bias.
 22. The method of claim 14, wherein therequesting local reputation engine originates the reputation queryresponsive to receiving a communication associated with an externalentity with respect to a protected enterprise network associated withthe requesting local reputation engine.
 23. The method of claim 22,wherein the requesting local reputation engine originates the reputationquery responsive to a local reputation associated with the externalentity being indeterminate.
 24. The method of claim 14, wherein derivingthe global reputation is further based upon public and privateinformation not available to any of the plurality of local reputationengines.
 25. One or more non-transitory computer readable storage mediahaving computer executable instructions operable to perform steps toaggregate a plurality of local reputation vectors to produce a globalreputation vector, the steps comprising: receiving a reputation queryfrom a requesting local reputation engine; retrieving a plurality oflocal reputations the local reputations being respectively associatedwith a plurality of local reputation engines; aggregating the pluralityof local reputations; deriving a global reputation from the aggregationof the local reputations; applying, for each local reputation enginethat originates a reputation query, a local reputation bias to theglobal reputation based on preferences of the local reputation engine togenerate a local reputation for the local reputation engine from theglobal reputation, so that for each local reputation engine a differentlocal reputation is generated from the global reputation; and respondingto the reputation query with the global reputation.
 26. Thenon-transitory computer readable storage media of claim 25 whereinderiving the global reputation is further based upon public or privateinformation available only to a central server.
 27. The non-transitorycomputer readable storage media of claim 25 further comprising computerexecutable instructions operable to aggregate reputation for a pluralityidentities associated with one or more of the plurality of entities. 28.A computer-implemented reputation system, the system comprising: acommunications interface operable to receive global reputationinformation from a central server, the central server being operable todetermine global reputations based upon feedback received from one ormore local reputation engines, the global reputation being respectivelyassociated with one or more entities; a reputation engine operable tobias the global reputation received from the central server based upondefined local preferences, the biasing comprising applying, for eachlocal reputation engine that originates a reputation query, a localreputation bias to the global reputation based on preferences of thelocal reputation engine to generate a local reputation for the localreputation engine from the global reputation, so that for each localreputation engine a different local reputation is generated from theglobal reputation; and wherein the centralized reputation engine isoperable to provide the global reputation of the queried entity to thecommunications interface responsive to receiving a reputation query fromsaid communications interface.